What's the meaning of dimensionality and what is it for this data?
May 5, 2015 · I've been told that dimensionality is usually referred to attributes or columns of the dataset. But in this case, does it include Class1 and Class2? and does dimensionality mean, the …
dimensionality reduction - Relationship between SVD and PCA. How to …
Jan 22, 2015 · However, it can also be performed via singular value decomposition (SVD) of the data matrix $\mathbf X$. How does it work? What is the connection between these two approaches? …
Why is Euclidean distance not a good metric in high dimensions?
May 20, 2014 · I read that 'Euclidean distance is not a good distance in high dimensions'. I guess this statement has something to do with the curse of dimensionality, but what exactly? Besides, what is 'high
Curse of dimensionality- does cosine similarity work better and if so ...
Apr 19, 2018 · When working with high dimensional data, it is almost useless to compare data points using euclidean distance - this is the curse of dimensionality. However, I have read that using …
Does SVM suffer from curse of high dimensionality? If no, Why?
Aug 23, 2020 · While I know that some of the classification techniques such as k-nearest neighbour classifier suffer from the curse of high dimensionality, I wonder does the same apply to the support …
Why is t-SNE not used as a dimensionality reduction technique for ...
Apr 13, 2018 · And Dimensionality reduction is also projection to a (hopefuly) meaningful space. But dimensionality reduction has to do so in a uninformed way -- it does not know what task you are …
Explain "Curse of dimensionality" to a child - Cross Validated
Aug 28, 2015 · The curse of dimensionality is that in higher dimensions, one either needs a much larger neighborhood for a given number of observations (which makes the notion of locality questionable) …
Why is dimensionality reduction always done before clustering?
I learned that it's common to do dimensionality reduction before clustering. But, is there any situation that it is better to do clustering first, and then do dimensionality reduction?
machine learning - Why is dimensionality reduction used if it almost ...
Jan 9, 2022 · So, the dimensionality reduction (ignoring years) is clearly best. However, if it turns out that you are in an inflationary periods, not so good monthly seasonal adjustment. However, a year …
How to decide if to do dimensionality reduction before clustering?
There are methods that simultaneously perform dimensionality reduction and clustering. These methods seek an optimally chosen low-dimensional representation so as to facilitate the identification of …